Source 1review2024Neurochemistry International
Toolkit/qRT-PCR
qRT-PCR
Also known as: qRT-PCR, qRTPCR, quantitative analysis of gene expression by qRTPCR
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
qRT-PCR is a quantitative reverse-transcription PCR assay used to measure transcript abundance, here applied to GFP mRNA during light-controlled gene expression in Synechococcus sp. PCC 7002. In the cited study, it quantified transcriptional activation and deactivation kinetics of optogenetic systems under green/red and light/dark illumination cycles.
Usefulness & Problems
Why this is useful
This assay is useful for resolving transcriptional responses of optogenetic circuits at the mRNA level under defined illumination programs. In the supplied evidence, it enabled kinetic measurement of GFP transcript changes and comparison of system performance across multiple green/red and light/dark cycles.
Source:
Monitoring GFP transcript levels allowed us to quantify the kinetics of transcriptional activation and deactivation and to test the effect of both multiple green/red and light/dark cycles on system performance.
Source:
In this protocol, an optogenetic expression system is used to achieve light-inducible gene expression in zebrafish embryos.
Problem solved
qRT-PCR addresses the need to quantify how rapidly and reversibly light-regulated gene expression systems change transcriptional output. In this context, it provided a way to measure activation and deactivation kinetics of GFP transcription in response to optogenetic stimulation.
Source:
In this protocol, an optogenetic expression system is used to achieve light-inducible gene expression in zebrafish embryos.
Problem links
qRT-PCR is a directly actionable nucleic-acid assay that could support environmental DNA or RNA workflows by validating and quantifying specific taxa or marker transcripts during biosphere sampling campaigns. It fits the gap's need for systematic cataloging better as a follow-up validation assay than as a primary discovery technology.
qRT-PCR is a fast, accessible assay for making specific transcripts visible and quantifying temporal expression changes. It is only a weak fit because the gap centers on scalable, highly multiplexed visibility of proteins, lipids, and metabolites rather than low-plex transcript measurement.
qRT-PCR could provide a fast, low-cost first-pass assay to compare expression of selected marker genes across candidate human-physiology models. That can help triage which models better match expected human tissue programs before deeper phenotyping.
confirms expression changes of candidate NAC genes identified from transcriptomic screening
LiteratureIt provides an orthogonal expression-validation step after transcriptomic candidate discovery. This helps confirm that selected NAC genes are upregulated during senescence.
Source:
It provides an orthogonal expression-validation step after transcriptomic candidate discovery. This helps confirm that selected NAC genes are upregulated during senescence.
measuring gene expression changes relevant to Alzheimer's disease
LiteratureIt helps researchers quantify gene-expression changes relevant to AD studies and diagnostic investigation.
Source:
It helps researchers quantify gene-expression changes relevant to AD studies and diagnostic investigation.
provides targeted validation of expression patterns observed in transcriptomics data
LiteratureIt addresses the need to confirm transcriptomics-based expression patterns with a targeted expression assay.
Source:
It addresses the need to confirm transcriptomics-based expression patterns with a targeted expression assay.
providing transcript-level measurement of optogenetic response kinetics
LiteratureIt adds transcript-level kinetic information that complements protein fluorescence measurements.
Source:
It adds transcript-level kinetic information that complements protein fluorescence measurements.
Published Workflows
Objective: Identify and characterize WRKY family members across four Araceae species and analyze stress- and tissue-associated expression of AkWRKY genes in Amorphophallus konjac.
Why it works: The workflow combines genome-scale identification and comparative characterization with expression profiling in A. konjac, then uses qRT-PCR to validate transcriptomics-derived expression patterns under tissues and stress conditions.
WRKY transcription factor involvement in biotic and abiotic stress responsesgene family expansion through segmental duplication under purifying selectionbioinformatics identificationcomparative genomicstranscriptomics expression profilingqRT-PCR validation
Stages
- 1.Bioinformatic identification and characterization of WRKY family members(in_silico_filter)
This stage establishes the comparative WRKY gene catalog and genomic context before downstream expression analysis.
Selection: Identification of WRKY family members in four Araceae species followed by characterization of physicochemical properties, gene structure, phylogenetic relationships, chromosomal distribution, collinearity, and cis-regulatory elements.
- 2.Transcriptomics-based expression analysis in Amorphophallus konjac(functional_characterization)
This stage identifies expression patterns in A. konjac and provides the basis for selecting genes or conditions for targeted validation.
Selection: Transcriptomics data were used to examine AkWRKY expression across tissues and stages of corm development.
- 3.qRT-PCR validation of AkWRKY expression under tissues and stress conditions(confirmatory_validation)
This stage confirms expression patterns observed in transcriptomics data using a targeted assay across biologically relevant treatments.
Selection: qRT-PCR was used to validate expression profiles across tissues, hormone treatments, Pcc infection, and abiotic stresses.
Objective: Identify NAC transcription factors associated with leaf senescence in Clerodendrum japonicum and functionally test whether prioritized candidates positively regulate senescence and ABA/dark-induced responses.
Why it works: The workflow first narrows candidates by differential expression during senescence, then validates expression patterns, and finally tests causality using gain-of-function and silencing assays in complementary systems.
mediation of ABA signaling pathwaysmediation of dark signaling pathwaystranscriptome sequencingcandidate screeningqRT-PCR validationheterologous overexpressionVIGS
Stages
- 1.Transcriptome-based candidate discovery(broad_screen)
This stage identifies candidate genes associated with leaf senescence before targeted validation and functional testing.
Selection: Differential expression between mature and early-senescent leaves in C. japonicum.
- 2.Expression-pattern validation(secondary_characterization)
This stage confirms that transcriptome-nominated NAC candidates show expression patterns consistent with senescence association.
Selection: qRT-PCR validation of candidate NAC gene expression patterns.
- 3.Functional perturbation characterization(functional_characterization)
This stage tests whether prioritized NAC candidates causally promote or delay senescence when increased or reduced in expression.
Selection: Functional testing of CjNAC43 and CjNAC54 by heterologous overexpression in Arabidopsis thaliana and VIGS in C. japonicum.
- 4.ABA- and dark-induced senescence testing(confirmatory_validation)
This stage tests whether the senescence-promoting role of the candidate NAC genes extends to ABA- and darkness-associated stress contexts.
Selection: Assessment of candidate gene roles under ABA- and dark-induced senescence conditions.
Steps
- 1.Sequence transcriptomes from mature and early-senescent leavesdiscovery assay
Identify genes differentially expressed between mature and early-senescent C. japonicum leaves.
This is the initial broad discovery step used to generate candidate senescence-associated genes before targeted validation.
- 2.Screen candidate NAC genes from transcriptomic results
Prioritize NAC family members associated with senescence from the transcriptomic dataset.
Candidate screening follows transcriptome generation because the sequencing output provides the pool from which NAC candidates are selected.
- 3.Validate candidate expression patterns by qRT-PCRexpression validation assay
Confirm expression patterns of candidate NAC genes identified from transcriptomic screening.
Expression validation is performed after candidate screening to confirm that prioritized genes show the expected senescence-associated expression pattern before functional testing.
- 4.Characterize CjNAC43 and CjNAC54 by heterologous overexpression in Arabidopsis thalianagenes under functional test
Test whether increased expression of CjNAC43 or CjNAC54 promotes senescence phenotypes.
After expression-based prioritization, gain-of-function testing provides causal evidence that the selected NAC genes can accelerate senescence.
- 5.Silence CjNAC43 or CjNAC54 in C. japonicum using VIGSloss-of-function validation method and targets
Test whether reducing CjNAC43 or CjNAC54 expression delays senescence in the native species.
This complementary loss-of-function step follows gain-of-function testing to strengthen causal inference and assess native-species relevance.
- 6.Assess roles of CjNAC43 and CjNAC54 in ABA- and dark-induced senescencegenes under stress-context validation
Determine whether the candidate NAC genes enhance sensitivity to ABA and darkness during senescence.
This confirmatory step follows core functional characterization to test whether the senescence-promoting effect extends to specific signaling contexts highlighted by the study.
Objective: Evaluate transfer and performance of imported optogenetic gene-expression control systems in Synechococcus sp. PCC 7002 and improve the better-performing system.
Why it works: The workflow compares imported optogenetic systems in the same cyanobacterial host, then uses protein- and transcript-level assays to identify the better-performing module and tune its output promoter for higher activity.
blue light-repressible gene expression controlgreen/red light-responsive gene expression controltranscriptional activation and deactivationheterologous system expressionGFP fluorescence assayqRT-PCRtargeted promoter engineering
Stages
- 1.Initial expression and performance characterization of imported optogenetic systems(functional_characterization)
This stage determines whether imported optogenetic systems function in the cyanobacterial host and identifies which system merits further optimization.
Selection: Performance of YF1/FixJ and CcaS/CcaR in Synechococcus sp. PCC 7002 measured by GFP fluorescence assays and qRT-PCR.
- 2.Kinetic and cycling characterization of the successful light-responsive system(secondary_characterization)
This stage characterizes temporal behavior beyond endpoint fluorescence output.
Selection: Quantify transcriptional activation and deactivation kinetics and test performance under multiple green/red and light/dark cycles.
- 3.Targeted promoter engineering to increase green-light activity(library_design)
This stage tunes the output promoter after identifying CcaS/CcaR as the better-performing system.
Selection: Targeted genetic modifications to the pCpcG2 output promoter to increase CcaS/CcaR activity under green light.
Steps
- 1.Express YF1/FixJ and CcaS/CcaR in Synechococcus sp. PCC 7002engineered optogenetic systems under test
Introduce candidate light-responsive gene-expression systems into the cyanobacterial host for comparison.
System expression is required before host-specific performance can be measured.
- 2.Characterize system performance using GFP fluorescence assays and qRT-PCRassays used for performance characterization
Measure output strength and transcript behavior of the expressed optogenetic systems.
Assays follow expression so the two systems can be compared in the same host context.
- 3.Monitor GFP transcript levels to quantify activation/deactivation kinetics and cycling behaviortranscript-level assay
Quantify temporal response properties and test repeated light-cycle performance.
After identifying a responsive system, transcript monitoring provides deeper temporal characterization than endpoint output alone.
- 4.Modify the pCpcG2 output promoter to increase CcaS/CcaR activity under green lightengineered output promoter and linked optogenetic system
Improve green-light-driven activity of the successful CcaS/CcaR system.
Promoter tuning was pursued after CcaS/CcaR was identified as the better-performing transferred system.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete measurement method used to characterize an engineered system.
Mechanisms
fluorescence-based nucleic acid quantificationpolymerase chain reaction amplificationreverse transcriptionTechniques
Functional AssayTarget processes
diagnostictranscriptionInput: Light
Implementation Constraints
cofactor dependency: cofactor requirement unknownencoding mode: genetically encodedimplementation constraint: context specific validationimplementation constraint: spectral hardware requirementoperating role: sensor
The evidence indicates use of qRT-PCR to monitor GFP transcript abundance in Synechococcus sp. PCC 7002 during green/red and light/dark illumination experiments. Beyond its basis in reverse transcription, PCR amplification, and fluorescence-based nucleic acid quantification, the supplied sources do not specify reagents, instrument settings, or construct design requirements.
The supplied evidence only supports use as a transcript quantification assay and does not provide details on assay sensitivity, normalization strategy, primer design, or absolute performance metrics. Validation in the provided claims is limited to GFP transcript monitoring in one cyanobacterial optogenetic context.
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Observations
successBacteriaapplication demoSynechococcus sp. PCC 7002
qRT-PCR
Inferred from claim claim3 during normalization. Monitoring GFP transcript levels by qRT-PCR allowed quantification of transcriptional activation and deactivation kinetics and testing of multiple green/red and light/dark cycles on system performance. Derived from claim claim3. Quoted text: Monitoring GFP transcript levels allowed us to quantify the kinetics of transcriptional activation and deactivation and to test the effect of both multiple green/red and light/dark cycles on system performance.
Source:
successBacteriaapplication demoSynechococcus sp. PCC 7002
qRT-PCR
Inferred from claim claim3 during normalization. Monitoring GFP transcript levels by qRT-PCR allowed quantification of transcriptional activation and deactivation kinetics and testing of multiple green/red and light/dark cycles on system performance. Derived from claim claim3. Quoted text: Monitoring GFP transcript levels allowed us to quantify the kinetics of transcriptional activation and deactivation and to test the effect of both multiple green/red and light/dark cycles on system performance.
Source:
successBacteriaapplication demoSynechococcus sp. PCC 7002
qRT-PCR
Inferred from claim claim3 during normalization. Monitoring GFP transcript levels by qRT-PCR allowed quantification of transcriptional activation and deactivation kinetics and testing of multiple green/red and light/dark cycles on system performance. Derived from claim claim3. Quoted text: Monitoring GFP transcript levels allowed us to quantify the kinetics of transcriptional activation and deactivation and to test the effect of both multiple green/red and light/dark cycles on system performance.
Source:
successBacteriaapplication demoSynechococcus sp. PCC 7002
qRT-PCR
Inferred from claim claim3 during normalization. Monitoring GFP transcript levels by qRT-PCR allowed quantification of transcriptional activation and deactivation kinetics and testing of multiple green/red and light/dark cycles on system performance. Derived from claim claim3. Quoted text: Monitoring GFP transcript levels allowed us to quantify the kinetics of transcriptional activation and deactivation and to test the effect of both multiple green/red and light/dark cycles on system performance.
Source:
successBacteriaapplication demoSynechococcus sp. PCC 7002
qRT-PCR
Inferred from claim claim3 during normalization. Monitoring GFP transcript levels by qRT-PCR allowed quantification of transcriptional activation and deactivation kinetics and testing of multiple green/red and light/dark cycles on system performance. Derived from claim claim3. Quoted text: Monitoring GFP transcript levels allowed us to quantify the kinetics of transcriptional activation and deactivation and to test the effect of both multiple green/red and light/dark cycles on system performance.
Source:
successBacteriaapplication demoSynechococcus sp. PCC 7002
qRT-PCR
Inferred from claim claim3 during normalization. Monitoring GFP transcript levels by qRT-PCR allowed quantification of transcriptional activation and deactivation kinetics and testing of multiple green/red and light/dark cycles on system performance. Derived from claim claim3. Quoted text: Monitoring GFP transcript levels allowed us to quantify the kinetics of transcriptional activation and deactivation and to test the effect of both multiple green/red and light/dark cycles on system performance.
Source:
successBacteriaapplication demoSynechococcus sp. PCC 7002
qRT-PCR
Inferred from claim claim3 during normalization. Monitoring GFP transcript levels by qRT-PCR allowed quantification of transcriptional activation and deactivation kinetics and testing of multiple green/red and light/dark cycles on system performance. Derived from claim claim3. Quoted text: Monitoring GFP transcript levels allowed us to quantify the kinetics of transcriptional activation and deactivation and to test the effect of both multiple green/red and light/dark cycles on system performance.
Source:
successBacteriaapplication demoSynechococcus sp. PCC 7002
qRT-PCR
Inferred from claim claim3 during normalization. Monitoring GFP transcript levels by qRT-PCR allowed quantification of transcriptional activation and deactivation kinetics and testing of multiple green/red and light/dark cycles on system performance. Derived from claim claim3. Quoted text: Monitoring GFP transcript levels allowed us to quantify the kinetics of transcriptional activation and deactivation and to test the effect of both multiple green/red and light/dark cycles on system performance.
Source:
Supporting Sources
Source 2primary paper2025MED
Source 3primary paper2021Journal of Visualized Experiments
Source 4primary paper2024MED
Ranked Claims
AkWRKY38 and AkWRKY53 exhibited high expression levels in Amorphophallus konjac under hormone treatments, Pcc infection, and abiotic stresses including low temperature, drought, and salt stress.
Fourteen AkWRKY genes showed significantly differential expression under ABA, JA, SA, Pectobacterium carotovorum subsp. carotovorum infection, low temperature, drought, and salt stress.
AkWRKY genes profiled by qRT-PCR under stress 14
Targeted genetic modifications to the pCpcG2 output promoter increased CcaS/CcaR system activity under green light.
Finally, we increased CcaS/CcaR system activity under green light through targeted genetic modifications to the pCpcG2 output promoter.
Targeted genetic modifications to the pCpcG2 output promoter increased CcaS/CcaR system activity under green light.
Finally, we increased CcaS/CcaR system activity under green light through targeted genetic modifications to the pCpcG2 output promoter.
Targeted genetic modifications to the pCpcG2 output promoter increased CcaS/CcaR system activity under green light.
Finally, we increased CcaS/CcaR system activity under green light through targeted genetic modifications to the pCpcG2 output promoter.
Targeted genetic modifications to the pCpcG2 output promoter increased CcaS/CcaR system activity under green light.
Finally, we increased CcaS/CcaR system activity under green light through targeted genetic modifications to the pCpcG2 output promoter.
Targeted genetic modifications to the pCpcG2 output promoter increased CcaS/CcaR system activity under green light.
Finally, we increased CcaS/CcaR system activity under green light through targeted genetic modifications to the pCpcG2 output promoter.
Targeted genetic modifications to the pCpcG2 output promoter increased CcaS/CcaR system activity under green light.
Finally, we increased CcaS/CcaR system activity under green light through targeted genetic modifications to the pCpcG2 output promoter.
Targeted genetic modifications to the pCpcG2 output promoter increased CcaS/CcaR system activity under green light.
Finally, we increased CcaS/CcaR system activity under green light through targeted genetic modifications to the pCpcG2 output promoter.
Targeted genetic modifications to the pCpcG2 output promoter increased CcaS/CcaR system activity under green light.
Finally, we increased CcaS/CcaR system activity under green light through targeted genetic modifications to the pCpcG2 output promoter.
Monitoring GFP transcript levels by qRT-PCR allowed quantification of transcriptional activation and deactivation kinetics and testing of multiple green/red and light/dark cycles on system performance.
Monitoring GFP transcript levels allowed us to quantify the kinetics of transcriptional activation and deactivation and to test the effect of both multiple green/red and light/dark cycles on system performance.
Monitoring GFP transcript levels by qRT-PCR allowed quantification of transcriptional activation and deactivation kinetics and testing of multiple green/red and light/dark cycles on system performance.
Monitoring GFP transcript levels allowed us to quantify the kinetics of transcriptional activation and deactivation and to test the effect of both multiple green/red and light/dark cycles on system performance.
Monitoring GFP transcript levels by qRT-PCR allowed quantification of transcriptional activation and deactivation kinetics and testing of multiple green/red and light/dark cycles on system performance.
Monitoring GFP transcript levels allowed us to quantify the kinetics of transcriptional activation and deactivation and to test the effect of both multiple green/red and light/dark cycles on system performance.
Monitoring GFP transcript levels by qRT-PCR allowed quantification of transcriptional activation and deactivation kinetics and testing of multiple green/red and light/dark cycles on system performance.
Monitoring GFP transcript levels allowed us to quantify the kinetics of transcriptional activation and deactivation and to test the effect of both multiple green/red and light/dark cycles on system performance.
Monitoring GFP transcript levels by qRT-PCR allowed quantification of transcriptional activation and deactivation kinetics and testing of multiple green/red and light/dark cycles on system performance.
Monitoring GFP transcript levels allowed us to quantify the kinetics of transcriptional activation and deactivation and to test the effect of both multiple green/red and light/dark cycles on system performance.
Monitoring GFP transcript levels by qRT-PCR allowed quantification of transcriptional activation and deactivation kinetics and testing of multiple green/red and light/dark cycles on system performance.
Monitoring GFP transcript levels allowed us to quantify the kinetics of transcriptional activation and deactivation and to test the effect of both multiple green/red and light/dark cycles on system performance.
Monitoring GFP transcript levels by qRT-PCR allowed quantification of transcriptional activation and deactivation kinetics and testing of multiple green/red and light/dark cycles on system performance.
Monitoring GFP transcript levels allowed us to quantify the kinetics of transcriptional activation and deactivation and to test the effect of both multiple green/red and light/dark cycles on system performance.
Monitoring GFP transcript levels by qRT-PCR allowed quantification of transcriptional activation and deactivation kinetics and testing of multiple green/red and light/dark cycles on system performance.
Monitoring GFP transcript levels allowed us to quantify the kinetics of transcriptional activation and deactivation and to test the effect of both multiple green/red and light/dark cycles on system performance.
In Synechococcus sp. PCC 7002, the CcaS/CcaR system responded well to light wavelengths and intensities and produced a 6-fold increase in protein fluorescence output after 30 min of green light.
the CcaS/CcaR system originating from the cyanobacterium Synechocystis sp. PCC 6803 responded well to light wavelengths and intensities, with a 6-fold increased protein fluorescence output observed after 30 min of green light.
protein fluorescence output increase 6 fold
In Synechococcus sp. PCC 7002, the CcaS/CcaR system responded well to light wavelengths and intensities and produced a 6-fold increase in protein fluorescence output after 30 min of green light.
the CcaS/CcaR system originating from the cyanobacterium Synechocystis sp. PCC 6803 responded well to light wavelengths and intensities, with a 6-fold increased protein fluorescence output observed after 30 min of green light.
protein fluorescence output increase 6 fold
In Synechococcus sp. PCC 7002, the CcaS/CcaR system responded well to light wavelengths and intensities and produced a 6-fold increase in protein fluorescence output after 30 min of green light.
the CcaS/CcaR system originating from the cyanobacterium Synechocystis sp. PCC 6803 responded well to light wavelengths and intensities, with a 6-fold increased protein fluorescence output observed after 30 min of green light.
protein fluorescence output increase 6 fold
In Synechococcus sp. PCC 7002, the CcaS/CcaR system responded well to light wavelengths and intensities and produced a 6-fold increase in protein fluorescence output after 30 min of green light.
the CcaS/CcaR system originating from the cyanobacterium Synechocystis sp. PCC 6803 responded well to light wavelengths and intensities, with a 6-fold increased protein fluorescence output observed after 30 min of green light.
protein fluorescence output increase 6 fold
In Synechococcus sp. PCC 7002, the CcaS/CcaR system responded well to light wavelengths and intensities and produced a 6-fold increase in protein fluorescence output after 30 min of green light.
the CcaS/CcaR system originating from the cyanobacterium Synechocystis sp. PCC 6803 responded well to light wavelengths and intensities, with a 6-fold increased protein fluorescence output observed after 30 min of green light.
protein fluorescence output increase 6 fold
In Synechococcus sp. PCC 7002, the CcaS/CcaR system responded well to light wavelengths and intensities and produced a 6-fold increase in protein fluorescence output after 30 min of green light.
the CcaS/CcaR system originating from the cyanobacterium Synechocystis sp. PCC 6803 responded well to light wavelengths and intensities, with a 6-fold increased protein fluorescence output observed after 30 min of green light.
protein fluorescence output increase 6 fold
In Synechococcus sp. PCC 7002, the CcaS/CcaR system responded well to light wavelengths and intensities and produced a 6-fold increase in protein fluorescence output after 30 min of green light.
the CcaS/CcaR system originating from the cyanobacterium Synechocystis sp. PCC 6803 responded well to light wavelengths and intensities, with a 6-fold increased protein fluorescence output observed after 30 min of green light.
protein fluorescence output increase 6 fold
In Synechococcus sp. PCC 7002, the CcaS/CcaR system responded well to light wavelengths and intensities and produced a 6-fold increase in protein fluorescence output after 30 min of green light.
the CcaS/CcaR system originating from the cyanobacterium Synechocystis sp. PCC 6803 responded well to light wavelengths and intensities, with a 6-fold increased protein fluorescence output observed after 30 min of green light.
protein fluorescence output increase 6 fold
In Synechococcus sp. PCC 7002, the YF1/FixJ system showed poor performance with a maximum dynamic range of 1.5-fold.
The YF1/FixJ system of non-cyanobacterial origin showed poor performance with a maximum dynamic range of 1.5-fold despite several steps to improve this.
maximum dynamic range 1.5 fold
In Synechococcus sp. PCC 7002, the YF1/FixJ system showed poor performance with a maximum dynamic range of 1.5-fold.
The YF1/FixJ system of non-cyanobacterial origin showed poor performance with a maximum dynamic range of 1.5-fold despite several steps to improve this.
maximum dynamic range 1.5 fold
In Synechococcus sp. PCC 7002, the YF1/FixJ system showed poor performance with a maximum dynamic range of 1.5-fold.
The YF1/FixJ system of non-cyanobacterial origin showed poor performance with a maximum dynamic range of 1.5-fold despite several steps to improve this.
maximum dynamic range 1.5 fold
In Synechococcus sp. PCC 7002, the YF1/FixJ system showed poor performance with a maximum dynamic range of 1.5-fold.
The YF1/FixJ system of non-cyanobacterial origin showed poor performance with a maximum dynamic range of 1.5-fold despite several steps to improve this.
maximum dynamic range 1.5 fold
In Synechococcus sp. PCC 7002, the YF1/FixJ system showed poor performance with a maximum dynamic range of 1.5-fold.
The YF1/FixJ system of non-cyanobacterial origin showed poor performance with a maximum dynamic range of 1.5-fold despite several steps to improve this.
maximum dynamic range 1.5 fold
In Synechococcus sp. PCC 7002, the YF1/FixJ system showed poor performance with a maximum dynamic range of 1.5-fold.
The YF1/FixJ system of non-cyanobacterial origin showed poor performance with a maximum dynamic range of 1.5-fold despite several steps to improve this.
maximum dynamic range 1.5 fold
In Synechococcus sp. PCC 7002, the YF1/FixJ system showed poor performance with a maximum dynamic range of 1.5-fold.
The YF1/FixJ system of non-cyanobacterial origin showed poor performance with a maximum dynamic range of 1.5-fold despite several steps to improve this.
maximum dynamic range 1.5 fold
In Synechococcus sp. PCC 7002, the YF1/FixJ system showed poor performance with a maximum dynamic range of 1.5-fold.
The YF1/FixJ system of non-cyanobacterial origin showed poor performance with a maximum dynamic range of 1.5-fold despite several steps to improve this.
maximum dynamic range 1.5 fold
The review presents qRT-PCR and iPSC-based mitochondrial-function evaluation as advances in diagnostic and research tools for Alzheimer's disease.
This study underlines the complexity of transferring optogenetic tools across species.
This study provides a detailed characterisation of the behaviour of the CcaS/CcaR system in Synechococcus sp. PCC 7002, as well as underlining the complexity of transferring optogenetic tools across species.
This study underlines the complexity of transferring optogenetic tools across species.
This study provides a detailed characterisation of the behaviour of the CcaS/CcaR system in Synechococcus sp. PCC 7002, as well as underlining the complexity of transferring optogenetic tools across species.
This study underlines the complexity of transferring optogenetic tools across species.
This study provides a detailed characterisation of the behaviour of the CcaS/CcaR system in Synechococcus sp. PCC 7002, as well as underlining the complexity of transferring optogenetic tools across species.
This study underlines the complexity of transferring optogenetic tools across species.
This study provides a detailed characterisation of the behaviour of the CcaS/CcaR system in Synechococcus sp. PCC 7002, as well as underlining the complexity of transferring optogenetic tools across species.
This study underlines the complexity of transferring optogenetic tools across species.
This study provides a detailed characterisation of the behaviour of the CcaS/CcaR system in Synechococcus sp. PCC 7002, as well as underlining the complexity of transferring optogenetic tools across species.
This study underlines the complexity of transferring optogenetic tools across species.
This study provides a detailed characterisation of the behaviour of the CcaS/CcaR system in Synechococcus sp. PCC 7002, as well as underlining the complexity of transferring optogenetic tools across species.
This study underlines the complexity of transferring optogenetic tools across species.
This study provides a detailed characterisation of the behaviour of the CcaS/CcaR system in Synechococcus sp. PCC 7002, as well as underlining the complexity of transferring optogenetic tools across species.
This study underlines the complexity of transferring optogenetic tools across species.
This study provides a detailed characterisation of the behaviour of the CcaS/CcaR system in Synechococcus sp. PCC 7002, as well as underlining the complexity of transferring optogenetic tools across species.
The TAEL/C120 system is used to achieve light-inducible gene expression in zebrafish embryos.
In this protocol, an optogenetic expression system is used to achieve light-inducible gene expression in zebrafish embryos.
The TAEL/C120 system is used to achieve light-inducible gene expression in zebrafish embryos.
In this protocol, an optogenetic expression system is used to achieve light-inducible gene expression in zebrafish embryos.
The TAEL/C120 system is used to achieve light-inducible gene expression in zebrafish embryos.
In this protocol, an optogenetic expression system is used to achieve light-inducible gene expression in zebrafish embryos.
The TAEL/C120 system is used to achieve light-inducible gene expression in zebrafish embryos.
In this protocol, an optogenetic expression system is used to achieve light-inducible gene expression in zebrafish embryos.
The TAEL/C120 system is used to achieve light-inducible gene expression in zebrafish embryos.
In this protocol, an optogenetic expression system is used to achieve light-inducible gene expression in zebrafish embryos.
The TAEL/C120 system is used to achieve light-inducible gene expression in zebrafish embryos.
In this protocol, an optogenetic expression system is used to achieve light-inducible gene expression in zebrafish embryos.
Blue light causes TAEL to dimerize, bind C120, and activate transcription.
When illuminated with blue light, TAEL dimerizes, binds to its cognate regulatory element called C120, and activates transcription.
Blue light causes TAEL to dimerize, bind C120, and activate transcription.
When illuminated with blue light, TAEL dimerizes, binds to its cognate regulatory element called C120, and activates transcription.
Blue light causes TAEL to dimerize, bind C120, and activate transcription.
When illuminated with blue light, TAEL dimerizes, binds to its cognate regulatory element called C120, and activates transcription.
Blue light causes TAEL to dimerize, bind C120, and activate transcription.
When illuminated with blue light, TAEL dimerizes, binds to its cognate regulatory element called C120, and activates transcription.
Blue light causes TAEL to dimerize, bind C120, and activate transcription.
When illuminated with blue light, TAEL dimerizes, binds to its cognate regulatory element called C120, and activates transcription.
Blue light causes TAEL to dimerize, bind C120, and activate transcription.
When illuminated with blue light, TAEL dimerizes, binds to its cognate regulatory element called C120, and activates transcription.
Blue light causes TAEL to dimerize, bind C120, and activate transcription.
When illuminated with blue light, TAEL dimerizes, binds to its cognate regulatory element called C120, and activates transcription.
Blue-light illumination induces GFP expression detectable after 30 minutes and reaching more than 130-fold induction after 3 hours in transgenic zebrafish embryos using the TAEL/C120 system.
induction of GFP expression can first be detected after 30 min of illumination and reaches a peak of more than 130-fold induction after 3 h of light treatment
peak GFP induction 130 foldtime to first detection of GFP induction 30 min
Blue-light illumination induces GFP expression detectable after 30 minutes and reaching more than 130-fold induction after 3 hours in transgenic zebrafish embryos using the TAEL/C120 system.
induction of GFP expression can first be detected after 30 min of illumination and reaches a peak of more than 130-fold induction after 3 h of light treatment
peak GFP induction 130 foldtime to first detection of GFP induction 30 min
Blue-light illumination induces GFP expression detectable after 30 minutes and reaching more than 130-fold induction after 3 hours in transgenic zebrafish embryos using the TAEL/C120 system.
induction of GFP expression can first be detected after 30 min of illumination and reaches a peak of more than 130-fold induction after 3 h of light treatment
peak GFP induction 130 foldtime to first detection of GFP induction 30 min
Blue-light illumination induces GFP expression detectable after 30 minutes and reaching more than 130-fold induction after 3 hours in transgenic zebrafish embryos using the TAEL/C120 system.
induction of GFP expression can first be detected after 30 min of illumination and reaches a peak of more than 130-fold induction after 3 h of light treatment
peak GFP induction 130 foldtime to first detection of GFP induction 30 min
Blue-light illumination induces GFP expression detectable after 30 minutes and reaching more than 130-fold induction after 3 hours in transgenic zebrafish embryos using the TAEL/C120 system.
induction of GFP expression can first be detected after 30 min of illumination and reaches a peak of more than 130-fold induction after 3 h of light treatment
peak GFP induction 130 foldtime to first detection of GFP induction 30 min
Blue-light illumination induces GFP expression detectable after 30 minutes and reaching more than 130-fold induction after 3 hours in transgenic zebrafish embryos using the TAEL/C120 system.
induction of GFP expression can first be detected after 30 min of illumination and reaches a peak of more than 130-fold induction after 3 h of light treatment
peak GFP induction 130 foldtime to first detection of GFP induction 30 min
The method is described as a versatile and easy-to-use approach for optogenetic gene expression.
This method is a versatile and easy-to-use approach for optogenetic gene expression.
The method is described as a versatile and easy-to-use approach for optogenetic gene expression.
This method is a versatile and easy-to-use approach for optogenetic gene expression.
The method is described as a versatile and easy-to-use approach for optogenetic gene expression.
This method is a versatile and easy-to-use approach for optogenetic gene expression.
The method is described as a versatile and easy-to-use approach for optogenetic gene expression.
This method is a versatile and easy-to-use approach for optogenetic gene expression.
The method is described as a versatile and easy-to-use approach for optogenetic gene expression.
This method is a versatile and easy-to-use approach for optogenetic gene expression.
The method is described as a versatile and easy-to-use approach for optogenetic gene expression.
This method is a versatile and easy-to-use approach for optogenetic gene expression.
Approval Evidence
5 sources4 linked approval claimsfirst-pass slugs qrt-pcr, quantitative-real-time-pcr, quantitative-real-time-pcr-qrt-pcr
These expression profiles were further validated by quantitative real-time PCR (qRT-PCR).
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We screened candidate NAC genes and validated their expression patterns using quantitative real-time PCR (qRT-PCR).
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From the quantitative analysis of gene expression by qRTPCR to the evaluation of mitochondrial function using induced pluripotent stem cells (iPSCs), the advances in diagnostic and research tools offer renewed hope.
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characterised their performance using GFP fluorescence assays and qRT-PCR
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Expression induction can be assessed by quantitative real-time PCR (qRT-PCR)
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candidate gene high expressionsupports
AkWRKY38 and AkWRKY53 exhibited high expression levels in Amorphophallus konjac under hormone treatments, Pcc infection, and abiotic stresses including low temperature, drought, and salt stress.
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stress responsive expressionsupports
Fourteen AkWRKY genes showed significantly differential expression under ABA, JA, SA, Pectobacterium carotovorum subsp. carotovorum infection, low temperature, drought, and salt stress.
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method capabilitysupports
Monitoring GFP transcript levels by qRT-PCR allowed quantification of transcriptional activation and deactivation kinetics and testing of multiple green/red and light/dark cycles on system performance.
Monitoring GFP transcript levels allowed us to quantify the kinetics of transcriptional activation and deactivation and to test the effect of both multiple green/red and light/dark cycles on system performance.
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research tool summarysupports
The review presents qRT-PCR and iPSC-based mitochondrial-function evaluation as advances in diagnostic and research tools for Alzheimer's disease.
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Comparisons
Source-stated alternatives
The paper pairs qRT-PCR with transcriptome sequencing for discovery and with overexpression/VIGS for functional testing.; Transcriptomics data were used upstream for expression analysis, with qRT-PCR serving as the validation approach in this paper.; The abstract contrasts qRT-PCR with iPSC-based mitochondrial-function evaluation as another research-tool modality, but does not provide a direct benchmark.
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The paper pairs qRT-PCR with transcriptome sequencing for discovery and with overexpression/VIGS for functional testing.
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Transcriptomics data were used upstream for expression analysis, with qRT-PCR serving as the validation approach in this paper.
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The abstract contrasts qRT-PCR with iPSC-based mitochondrial-function evaluation as another research-tool modality, but does not provide a direct benchmark.
Source-backed strengths
The cited evidence supports qRT-PCR as a sensitive functional readout for transcript-level dynamics during repeated illumination cycling. It was specifically used to quantify kinetic responses and assess performance of light-responsive systems in Synechococcus sp. PCC 7002.
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Finally, we increased CcaS/CcaR system activity under green light through targeted genetic modifications to the pCpcG2 output promoter.
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the CcaS/CcaR system originating from the cyanobacterium Synechocystis sp. PCC 6803 responded well to light wavelengths and intensities, with a 6-fold increased protein fluorescence output observed after 30 min of green light.
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The YF1/FixJ system of non-cyanobacterial origin showed poor performance with a maximum dynamic range of 1.5-fold despite several steps to improve this.
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induction of GFP expression can first be detected after 30 min of illumination and reaches a peak of more than 130-fold induction after 3 h of light treatment
Compared with open-source microplate reader
qRT-PCR and open-source microplate reader address a similar problem space because they share transcription.
Shared frame: same top-level item type; shared target processes: transcription; same primary input modality: light
Strengths here: appears more independently replicated; looks easier to implement in practice.
Compared with Raman spectroscopy
qRT-PCR and Raman spectroscopy address a similar problem space because they share diagnostic.
Shared frame: same top-level item type; shared target processes: diagnostic; same primary input modality: light
Strengths here: appears more independently replicated.
Compared with transcriptional analysis
qRT-PCR and transcriptional analysis address a similar problem space because they share transcription.
Shared frame: same top-level item type; shared target processes: transcription; same primary input modality: light
Strengths here: appears more independently replicated; looks easier to implement in practice.
Ranked Citations
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